Given a spatiotemporal (ST) dataset and a path in its embedding spatiotemporal framework, the goal is to to identify all interesting sub-paths defined by an interest measure. Sub-path discovery is of fundamental importance for understanding climate changes, agriculture, and many other application. However, this problem is computationally challenging due to the massive volume of data, the varying length of sub-paths and non-monotonicity of interestingness throughout a sub-path. Previous approaches find interesting unit sub-paths (e.g., unit time interval) or interesting points. By contrast, we propose a Sub-path Enumeration and Pruning (SEP) approach that finds collections of long interesting subpaths. Two case studies using climate change datasets show that SEP can find long interesting sub-paths which represent abrupt climate change. We provide theoretical analyses of correctness, completeness and computational complexity of the proposed approach. We also provide experimental evaluation of two traversal strategies for enumerating and pruning candidate sub-paths.

Abstract. Given a collection of sensors monitoring a flow network, the problem of discovering teleconnected flow anomalies aims to identify strongly connected pairs of events (e.g., introduction of a contaminant and its removal from a river). The ability to mine teleconnected flow anomalies is important for applications related to environmental science, video surveillance, and transportation systems. However, this problem is computationally hard because of the large number of time instants of measurement, sensors, and locations. This paper characterizes the computational structure in terms of three critical tasks, (1) detection of flow anomaly events, (2) identification of candidate pairs of events, and (3) evaluation of candidate pairs for possible teleconnection. The first task was addressed in our recent work. In this paper, we propose a RAD (Relationship Analysis of spatio-temporal Dynamic neighborhoods) approach for steps 2 and 3 to discover teleconnected flow anomalies. Computational overhead is brought down significantly by utilizing our proposed spatio-temporal dynamic neighborhood model as an index and a pruning strategy. We prove correctness and completeness for the proposed approaches. We also experimentally show the efficacy of our proposed methods using both synthetic and real datasets. 1

License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Anomaly detection systems and many other applications are frequently confronted with the problem of finding the largest knee point in the sorted curve for a set of unsorted points. This paper proposes an efficient knee point search algorithm with minimized time complexity using the cascading top-k sorting when a priori probability distribution of the knee point is known. First, a top-k sort algorithm is proposed based on a quicksort variation. We divide the knee point search problem into multiple steps. And in each step an optimization problem of the selection number k is solved, where the objective function is defined as the expected time cost. Because the expected time cost in one step is dependent on that of the afterwards steps, we simplify the optimization problem by minimizing the maximum expected time cost. The posterior probability of the largest knee point distribution and the other parameters are updated before solving the optimization problem in each step. An example of source detection of DNS DoS flooding attacks is provided to illustrate the applications of the proposed algorithm. 1.